Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.5
2.1
Journals
Algorithms
Special Issues
Mathematical Modelling in Engineering and Human Behaviour (4th Edition)
share announcement

Mathematical Modelling in Engineering and Human Behaviour (4th Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 2573

Special Issue Editors

Prof. Dr. Alicia Cordero

E-Mail Website
Guest Editor
School of Telecommunications Engineering, Universitat Politècnica de València, 46022 Valencia, Spain
Interests: numerical analysis; iterative methods; nonlinear problems; discrete dynamics; real and complex
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Juan Ramón Torregrosa Sánchez

E-Mail Website
Guest Editor
Institute for Multidisciplinary Mathematics, Universitat Politècnica de València, 46022 València, Spain
Interests: iterative processes; matrix analysis; numerical analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Mathematical Modelling in Engineering and Human Behaviour (4th Edition)”, will develop an interdisciplinary forum for research in medicine, sociology, business and engineering, where experts in cross-disciplinary areas can discuss recent advances in mathematical techniques in a common and understandable language. This Special Issue will connect researchers who utilize mathematics for formulating and analyzing models.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Mathematical models in epidemiology and medicine;
  • Mathematical models in engineering;
  • Applications of linear algebra;
  • Iterative methods for nonlinear problems;
  • Simulations in civil engineering and railway engineering;
  • Networks and applications;
  • Financial mathematics;
  • Uncertainty quantification and modelling;
  • Optimization, least squares and applications;
  • Machine learning and neuronal networks;
  • Mathematics for decision-making.

Prof. Dr. Alicia Cordero
Prof. Dr. Juan Ramón Torregrosa Sánchez
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematical models in epidemiology and medicine
  • mathematical models in engineering
  • applications of linear algebra
  • iterative methods for nonlinear problems
  • simulations in civil engineering and railway engineering
  • networks and applications
  • financial mathematics
  • uncertainty quantification and modelling optimization, least squares and applications
  • machine learning and neuronal networks
  • mathematics for decision making

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

25 pages, 46400 KB  
Article
ALIGN: An AI-Driven IoT Framework for Real-Time Sitting Posture Detection
by Kunal Kumar Sahoo, Tanish Patel, Debabrata Swain, Vassilis C. Gerogiannis, Andreas Kanavos, Davinder Paul Singh, Manish Kumar and Biswaranjan Acharya
Algorithms 2026, 19(1), 48; https://doi.org/10.3390/a19010048 - 5 Jan 2026
Viewed by 562
Abstract
Posture, defined as the body’s alignment relative to gravity, plays a vital role in musculoskeletal health by influencing muscle efficiency, joint integrity, and overall balance. The global shift to remote and sedentary work environments during the COVID-19 pandemic has amplified concerns regarding posture-related [...] Read more.
Posture, defined as the body’s alignment relative to gravity, plays a vital role in musculoskeletal health by influencing muscle efficiency, joint integrity, and overall balance. The global shift to remote and sedentary work environments during the COVID-19 pandemic has amplified concerns regarding posture-related disorders and long-term ergonomic risks. This study introduces ALIGN, an IoT-enabled intelligent system for real-time sitting posture detection that integrates both machine learning and deep learning methodologies. Implemented on a single-board computer, the system processes live video streams to classify user posture as correct or incorrect and provides alert-based notifications when sustained improper posture is detected, thereby supporting real-time posture awareness without issuing corrective instructions. Among conventional classifiers, K-Nearest Neighbors (KNN), Support Vector Classifiers (SVC), and Multi-Layer Perceptrons (MLP) achieved accuracies of 98.74%, 96.64%, and 97.17%, respectively, while in the deep learning category, ResNet52 reached a test accuracy of 94.37%, outperforming DenseNet121 (81.53%). By enabling intelligent real-time detection and monitoring, ALIGN offers a scalable and cost-effective solution for ergonomic risk awareness and preventive digital health support. Full article
(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (4th Edition))
►▼ Show Figures

Figure 1

18 pages, 436 KB  
Article
A Newton-Based Tuna Swarm Optimization Algorithm for Solving Nonlinear Problems with Application to Differential Equations
by Aanchal Chandel, Sonia Bhalla, Alicia Cordero, Juan R. Torregrosa and Ramandeep Behl
Algorithms 2026, 19(1), 40; https://doi.org/10.3390/a19010040 - 4 Jan 2026
Viewed by 195
Abstract
This paper presents two novel hybrid iterative schemes that combine Newton’s method and its variant with the Tuna Swarm Optimization (TSO) algorithm, aimed at solving complex nonlinear equations with enhanced accuracy and efficiency. Newton’s method is renowned for its rapid convergence in root-finding [...] Read more.
This paper presents two novel hybrid iterative schemes that combine Newton’s method and its variant with the Tuna Swarm Optimization (TSO) algorithm, aimed at solving complex nonlinear equations with enhanced accuracy and efficiency. Newton’s method is renowned for its rapid convergence in root-finding problems, and it is integrated with TSO, a recent swarm intelligence algorithm that surpasses the complex behavior of tuna fish in order to optimize the search for superior solutions. These hybrid methods are reliable and efficient for solving challenging mathematical and applied science problems. Several numerical experiments and applications involving ordinary differential equations have been carried out to demonstrate the superiority of the proposed hybrid methods in terms of convergence rate, accuracy, and robustness compared to traditional optimization and iterative methods. The stability and efficiency of the proposed methods have also been verified. The results indicate that the hybrid approaches outperform traditional methods, making them a promising tool for solving a wide range of mathematical and engineering problems. Full article
(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (4th Edition))
►▼ Show Figures

Figure 1

42 pages, 1313 KB  
Article
Adaptive Parallel Methods for Polynomial Equations with Unknown Multiplicity
by Mudassir Shams and Bruno Carpentieri
Algorithms 2026, 19(1), 21; https://doi.org/10.3390/a19010021 - 24 Dec 2025
Viewed by 256
Abstract
New two-step simultaneous iterative techniques are proposed for solving polynomial equations with multiple roots of unknown multiplicity. The developed schemes achieve a local convergence order of ten and address key limitations of existing solvers, namely their dependence on prior multiplicity information and their [...] Read more.
New two-step simultaneous iterative techniques are proposed for solving polynomial equations with multiple roots of unknown multiplicity. The developed schemes achieve a local convergence order of ten and address key limitations of existing solvers, namely their dependence on prior multiplicity information and their reduced efficiency when dealing with clustered or repeated roots. Root multiplicities are adaptively estimated within the iterative process, avoiding additional function evaluations beyond those required for parallel updates. The robustness and stability of the proposed methods are assessed using both random and distant initial guesses and validated on benchmark polynomials as well as nonlinear models from biomedical engineering. The numerical results show notable improvements in residual error, iteration count, CPU time, memory usage, and overall convergence rate compared with established classical techniques. These findings demonstrate that the proposed schemes provide reliable, high-order, and computationally efficient tools for solving challenging nonlinear problems in science and engineering. Full article
(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (4th Edition))
►▼ Show Figures

Figure 1

28 pages, 7635 KB  
Article
A Hybrid Machine Learning Framework for Predicting Hurricane Losses in Parametric Insurance with Highly Imbalanced Data
by Yangchongyi Men, Roberto Guidotti, Javier A. Cuartas-Micieces, Angel A. Juan, Guillermo Franco, Patricia Carracedo and Laura Lemke-Verderame
Algorithms 2026, 19(1), 15; https://doi.org/10.3390/a19010015 - 23 Dec 2025
Viewed by 389
Abstract
This paper proposes a novel methodology, based on machine learning and statistical models, for predicting hurricane-related losses to specific assets. Our approach uses three critical storm parameters typically tracked by meteorological agencies: maximum wind speed, minimum sea level pressure, and radius of maximum [...] Read more.
This paper proposes a novel methodology, based on machine learning and statistical models, for predicting hurricane-related losses to specific assets. Our approach uses three critical storm parameters typically tracked by meteorological agencies: maximum wind speed, minimum sea level pressure, and radius of maximum wind. The system categorizes potential damage events into three insurance-relevant classes: non-payable, partially payable, and fully payable. Three triggers for final payouts were designed: hybrid framework, standalone regression, and standalone non-linear regression. The hybrid framework combines two classification models and a non-linear regression model in an ensemble specifically designed to minimize the absolute differences between predicted and actual payouts (Total Absolute Error or TAE), addressing highly imbalanced and partially compensable events. Although this complex approach may not be suitable for all current contracts due to limited interpretability, it provides an approximate lower bound for the minimization of the absolute error. The standalone non-linear regression model is structurally simpler, yet it likewise offers limited transparency. This hybrid framework is not intended for direct deployment in parametric insurance contracts, but rather serves as a benchmarking and research tool to quantify the achievable reduction in basis risk under highly imbalanced conditions. The standalone linear regression provides an interpretable linear regression model optimized for feature selection and interaction terms, enabling direct deployment in parametric insurance contracts while maintaining transparency. These three approaches allow analysis of the trade-off between model complexity, predictive performance, and interpretability. The three approaches are compared using comprehensive hurricane simulation data from an industry-standard catastrophe model. The methodology is particularly valuable for parametric insurance applications, where rapid assessment and claims settlement are essential. Full article
(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (4th Edition))
►▼ Show Figures

Figure 1

29 pages, 2616 KB  
Article
Adaptive Real-Time Planning of Trailer Assignments in High-Throughput Cross-Docking Terminals
by Tamás Bányai and Sebastian Trojahn
Algorithms 2025, 18(11), 679; https://doi.org/10.3390/a18110679 - 24 Oct 2025
Viewed by 716
Abstract
Cross-docking has emerged as a critical logistics strategy to reduce lead times, lower inventory levels, and enhance supply chain responsiveness. However, in high-throughput terminals, efficient coordination of inbound and outbound trailers remains a complex task, especially under uncertain and dynamically changing conditions. We [...] Read more.
Cross-docking has emerged as a critical logistics strategy to reduce lead times, lower inventory levels, and enhance supply chain responsiveness. However, in high-throughput terminals, efficient coordination of inbound and outbound trailers remains a complex task, especially under uncertain and dynamically changing conditions. We propose a practical framework that helps logistics terminals assign trailers to docks in real time. It links live sensor data with a mathematical optimization model, so that the system can quickly adjust trailer plans when traffic or workload changes. Real-time data from IoT sensors, GPS, and operational records are preprocessed, enriched with predictive analytics, and used as input for a Mixed-Integer Linear Programming (MILP) model solved in rolling horizons. This enables the continuous reallocation of inbound and outbound trailers, ensuring synchronized flows and balanced dock utilization. Numerical experiments compare the adaptive approach with conventional first-come-first-served scheduling. Results show that average inbound dock utilization improves from 68% to 71%, while the share of periods with full utilization increases from 33.3% to 41.4%. Outbound utilization also rises from 57% to 62%. Moreover, trailer delays are significantly reduced, and the overall makespan shortens from 45 to 40 time slots. These findings confirm that adaptive, real-time trailer assignment can enhance efficiency, reliability, and resilience in cross-docking operations. The proposed framework thus bridges the gap between static optimization models and the operational requirements of modern, high-throughput logistics hubs. Full article
(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (4th Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop